Speed of computing using sage in different platforms: User experiences

I want to know from the users of sage, their feeling on handling computations using sage on different platforms. Actually I have a windows pc and sage works by virtual machine inside it. So the computations are very slow. I am trying to compute the ray class groups associated with number fields. But the problem is: Somehow using virtual machine Sage runs too slowly, it even takes time to enter the data by the keyboard.

So I am thinking to buy a computer on some other platform (Ubuntu or Apple macbooks) (within around 1000 euro ) so that I can make some computations in a reasonable speed. I like to have user experiences on this issue.

Comments

May be building Sage from source will give a better performance? Also, a performance of underlying libraries may be an issue; once I have encountered significant performance issue because of unoptimized linear algebra library, see this topic

1 answer

Indeed, the emulation costs some ressources, since you run the following stack: Windows/Virtualbox/Linux/Sage.

Ubuntu and windows are not platforms but exploitation systems that can run more-or-less on the same platforms. So it does not makes much sense to buy a new computer to run Ubuntu (or any GNU/Linux distro) on it. Instead you can:

Replace windows by Ubuntu (say) on your existing computer, in case you do not use windows-specific software.

Install Ubuntu on a separate partition, so that you can boot on both operating systems.

If you do not want to touch your hard disk, you can boot from a live-USB containing Debian GNU/Linux and Sage and run Sage from there. See http://www.sagemath.org/download-live... Note that in this case, Sage is built to run on both 32bit and 64bit platforms so that even old computers can run it. Hence you might lose some performance compared to versions of Sage compiled for 64bit systems, i am thinking on building such 64bit-only live USB.

Out of curiosity, could you please send us the code you are trying to execute on your computer ? Sometimes, tuning your code is more efficient than buying a newer computer.